Automatic Parameter Tuning using Bayesian Optimization Method

Changwu HUANG; Bo YUAN; Yuanxiang LI; Xin YAO

doi:10.1109/CEC.2019.8789891

Automatic Parameter Tuning using Bayesian Optimization Method

Changwu HUANG, Bo YUAN, Yuanxiang LI, Xin YAO

Research output: Book Chapters | Papers in Conference Proceedings › Conference paper (refereed) › Research › peer-review

20 Citations (Scopus)

Abstract

The Capacitated Arc Routing Problem (CARP) is an essential and challenging problem in smart logistics. Parameter tuning is commonly encountered in designing and applying heuristic or meta-heuristic algorithms for CARP. Recently, automatic parameter tuning or hyper-parameter optimization, which focuses on automatically finding an optimal parameter setting of an algorithm for problems at hand, has attracted considerable attention and become popular for addressing parameter tuning problems. This paper studies automatic parameter tuning for advanced algorithms in solving CARP. When designing algorithms for CARP, parameters are usually determined through empirical analysis or following some rules of thumb. This paper uses an automatic parameter tuning approach, that is, Bayesian optimization method, to tune an algorithm called SAHiD, which is a scalable approach based on hierarchical decomposition for large-scale CARP. The experimental results show that the algorithm's performance can be significantly improved with automatic parameter tuning. The tuned SAHiD algorithm obtains better solutions and faster convergence speed than original SAHiD on test CARP instances. © 2019 IEEE.

Original language	English
Title of host publication	2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	2090-2097
Number of pages	8
ISBN (Print)	9781728121536
DOIs	https://doi.org/10.1109/CEC.2019.8789891
Publication status	Published - Jun 2019
Externally published	Yes

Funding

This work was supported by National Key R&D Program of China (Grant No. 2017YFC0804003), the Program for Guangdong Introducing Innovative and Enter-preneurial Teams (Grant No. 2017ZT07X386), Shenzhen Peacock Plan (Grant No. KQTD2016112514355531), the Program for University Key Laboratory of Guangdong Province (Grant No. 2017KSYS008), and the Science and Technology Innovation Committee Foundation of Shenzhen (Grant Nos. ZDSYS201703031748284, JCYJ20170307105521943, JCYJ20170817112421757).

Keywords

automatic parameter tuning
Bayesian optimization
Capacitated arc routing problem (CARP)

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10.1109/CEC.2019.8789891

Cite this

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title = "Automatic Parameter Tuning using Bayesian Optimization Method",

abstract = "The Capacitated Arc Routing Problem (CARP) is an essential and challenging problem in smart logistics. Parameter tuning is commonly encountered in designing and applying heuristic or meta-heuristic algorithms for CARP. Recently, automatic parameter tuning or hyper-parameter optimization, which focuses on automatically finding an optimal parameter setting of an algorithm for problems at hand, has attracted considerable attention and become popular for addressing parameter tuning problems. This paper studies automatic parameter tuning for advanced algorithms in solving CARP. When designing algorithms for CARP, parameters are usually determined through empirical analysis or following some rules of thumb. This paper uses an automatic parameter tuning approach, that is, Bayesian optimization method, to tune an algorithm called SAHiD, which is a scalable approach based on hierarchical decomposition for large-scale CARP. The experimental results show that the algorithm's performance can be significantly improved with automatic parameter tuning. The tuned SAHiD algorithm obtains better solutions and faster convergence speed than original SAHiD on test CARP instances. {\textcopyright} 2019 IEEE.",

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author = "Changwu HUANG and Bo YUAN and Yuanxiang LI and Xin YAO",

year = "2019",

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doi = "10.1109/CEC.2019.8789891",

language = "English",

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Automatic Parameter Tuning using Bayesian Optimization Method. / HUANG, Changwu; YUAN, Bo; LI, Yuanxiang et al.
2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2019. p. 2090-2097 8789891.

Research output: Book Chapters | Papers in Conference Proceedings › Conference paper (refereed) › Research › peer-review

TY - GEN

T1 - Automatic Parameter Tuning using Bayesian Optimization Method

AU - HUANG, Changwu

AU - YUAN, Bo

AU - LI, Yuanxiang

AU - YAO, Xin

PY - 2019/6

Y1 - 2019/6

N2 - The Capacitated Arc Routing Problem (CARP) is an essential and challenging problem in smart logistics. Parameter tuning is commonly encountered in designing and applying heuristic or meta-heuristic algorithms for CARP. Recently, automatic parameter tuning or hyper-parameter optimization, which focuses on automatically finding an optimal parameter setting of an algorithm for problems at hand, has attracted considerable attention and become popular for addressing parameter tuning problems. This paper studies automatic parameter tuning for advanced algorithms in solving CARP. When designing algorithms for CARP, parameters are usually determined through empirical analysis or following some rules of thumb. This paper uses an automatic parameter tuning approach, that is, Bayesian optimization method, to tune an algorithm called SAHiD, which is a scalable approach based on hierarchical decomposition for large-scale CARP. The experimental results show that the algorithm's performance can be significantly improved with automatic parameter tuning. The tuned SAHiD algorithm obtains better solutions and faster convergence speed than original SAHiD on test CARP instances. © 2019 IEEE.

AB - The Capacitated Arc Routing Problem (CARP) is an essential and challenging problem in smart logistics. Parameter tuning is commonly encountered in designing and applying heuristic or meta-heuristic algorithms for CARP. Recently, automatic parameter tuning or hyper-parameter optimization, which focuses on automatically finding an optimal parameter setting of an algorithm for problems at hand, has attracted considerable attention and become popular for addressing parameter tuning problems. This paper studies automatic parameter tuning for advanced algorithms in solving CARP. When designing algorithms for CARP, parameters are usually determined through empirical analysis or following some rules of thumb. This paper uses an automatic parameter tuning approach, that is, Bayesian optimization method, to tune an algorithm called SAHiD, which is a scalable approach based on hierarchical decomposition for large-scale CARP. The experimental results show that the algorithm's performance can be significantly improved with automatic parameter tuning. The tuned SAHiD algorithm obtains better solutions and faster convergence speed than original SAHiD on test CARP instances. © 2019 IEEE.

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BT - 2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

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Automatic Parameter Tuning using Bayesian Optimization Method

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